测井技术2017，Vol.41Issue(2)：176-182,7.DOI:10.16489/j.issn.1004-1338.2017.02.009
联合神经网络在储层参数预测中的研究与应用
Research on Committee Neural Network Model for Reservoir Physical Parameter Prediction
摘要
Abstract
Geological reservoir physical parameters are crucial for building the three-dimensional geological model,and reservoir physical parameters are often obtained from logging data.In conventional logging interpretation,reservoir physical parameters are calculated by empirical formula or simplified geological conditions.The development of new technology has brought a new way for the prediction of reservoir physical parameters.This paper presents committee neural network (CNN),a new neural network model,which is based on BP neural network,RBF neural network,support vector regression and single layer perception.This model could adjust network structure automatically and get the optimal predicted value,which avoids the defects of individual neural network in parameters prediction and improves the accuracy of the prediction.The model is used and tested in three wells logging in the same area.One well is used to establish the CNN model,and two wells are used to assess the reliability of constructed CNN model.Results show that the CNN model is better than individual neural network model.关键词
储层参数预测/联合神经网络/BP神经网络/RBF神经网络/支持向量回归Key words
reservoir parameters prediction/committee neural network/BP neural network/RBF neural network/support vector regression分类
天文与地球科学引用本文复制引用
段友祥,李根田..联合神经网络在储层参数预测中的研究与应用[J].测井技术,2017,41(2):176-182,7.基金项目
国家科技重大专项(2011ZX0511-003) （2011ZX0511-003）
